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Main Authors: Wang, Xiaolong, Li, Biaolin, Wang, Xiaoli
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2604.11268
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author Wang, Xiaolong
Li, Biaolin
Wang, Xiaoli
author_facet Wang, Xiaolong
Li, Biaolin
Wang, Xiaoli
contents As a special type of bilinear systems, K-power bilinear systems possess a special coupled structure along with nice properties in practice. In this paper, we investigate the data-driven counterpart of balanced truncation for K-power systems. As the standard balanced truncation is performed based on the subsystems of K-power systems, the main idea is to approximate the quantities of each reduced subsystem with the evaluations of transfer functions. We exploit the nice properties of Gramians for K-power systems, and establish the explicit relationship between the main quantities of balanced truncation and the evaluation of transfer functions. As a result, reduced models produced via balanced truncation can be assembled approximately by the sample data of transfer functions, leading to a data-driven balancing truncation method for K-power systems. An advanced procedure is also provided to avoid the complex arithmetic completely and produce real-valued reduced models. Two numerical examples confirm the feasibility and effectiveness of the proposed method.
format Preprint
id arxiv_https___arxiv_org_abs_2604_11268
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Data-driven balanced truncation of K-power bilinear systems
Wang, Xiaolong
Li, Biaolin
Wang, Xiaoli
Optimization and Control
As a special type of bilinear systems, K-power bilinear systems possess a special coupled structure along with nice properties in practice. In this paper, we investigate the data-driven counterpart of balanced truncation for K-power systems. As the standard balanced truncation is performed based on the subsystems of K-power systems, the main idea is to approximate the quantities of each reduced subsystem with the evaluations of transfer functions. We exploit the nice properties of Gramians for K-power systems, and establish the explicit relationship between the main quantities of balanced truncation and the evaluation of transfer functions. As a result, reduced models produced via balanced truncation can be assembled approximately by the sample data of transfer functions, leading to a data-driven balancing truncation method for K-power systems. An advanced procedure is also provided to avoid the complex arithmetic completely and produce real-valued reduced models. Two numerical examples confirm the feasibility and effectiveness of the proposed method.
title Data-driven balanced truncation of K-power bilinear systems
topic Optimization and Control
url https://arxiv.org/abs/2604.11268